Digital communications often require timing information to be extracted from a data stream. This data stream is subject to the degradation of the communication channel medium. Since the timing information is shared with the data, it is subject to inter-symbol interference (ISI). This ISI requires specific filtering to optimize the timing recovery performance, such as described in U.S. Pat. No. 6,975,676.
Many different filter structures have been used to correct for this timing ISI. A general example diagram of such a system is shown in FIG. 1A where an input signal 104 is sampled by sampler 102, using an eye-edge-synchronized reference 106, and by sampler 112, using an eye-center-synchronized reference 116. The output of these samplers goes through an eye edge equalizer 103 and an eye center equalizer 105, respectively. The equalized samples 107 and 109 are used by the timing recovery circuit to adjust the phase of a reference clock 111 to generate the eye-edge-synchronized reference 106 and eye-center-synchronized reference 116.
However, the known structures have not been optimized for the specific characteristics of the timing recovery. Different coding techniques lead to different clock extraction methods.
Feed-Forward Equalizers (FFEs) have been used for filtering. The usual structure is to use future and past samples taken at integer multiples of the unit interval time (T) of the data stream, weight them according to the filter coefficients and sum them together, as shown in the FFE 10 in FIG. 1B. Future samples, also referred to as pre-cursors, refer to samples coming before the current sample being corrected. They require the current sample to be delayed before it is processed, in order for these future samples to be captured. Past samples are also referred to as post-cursors.
Fractional FFEs also exist that additionally sample the data stream at fractions of the unit interval (UI), for example twice per UI (sampling at T/2). The performance of the filter generally improves as the length of the filter increases, however with diminishing returns. While using fractional FFEs can be beneficial, the number samples required to cover a comparable filter depth increases in proportion to the increased sampling rate. Furthermore, using fractional FFEs requires more input samplers and more reference clock generation, which increases complexity.
It is, therefore, desirable to provide a filter configuration that balances performance and complexity.